Search Results (100)

The increasing interconnectivity and digital transformation of Communication, Navigation, and Surveillance (CNS) systems have expanded their attack surface, rendering traditional perimeter-based security models inadequate for protecting these critical infrastructures. Zero Trust Architecture (ZTA), founded on the principle of “never trust, always verify,” offers a paradigm shift towards continuous, context-aware security. This paper presents a literature review investigating the application of ZTA principles to secure modern CNS ecosystems, following the guidelines of the International Civil Aviation Organization (ICAO) through its Cybersecurity Strategy and Plan. We analyze the alignment of ZTA core tenets—such as least-privilege access, micro-segmentation, and continuous authentication—with the unique operational requirements of CNS systems. This paper also presents a cybersecurity framework, under development within the Future Communications Digital Infrastructure (FCDI) project of the SESAR JU program, which aims to assist CNS stakeholders in collaboratively identifying cybersecurity threats within their scope of responsibility. The review critically examines implementation challenges for specific CNS subsystems: secure aeronautical communications (e.g., LDACS), resilient PNT (Positioning, Navigation, and Timing) services, and integrated surveillance networks (e.g., ADS-B, multilateration). Furthermore, we identify and evaluate domain-specific challenges, including integration with legacy avionics and ground systems, managing stringent latency and reliability constraints, and protecting against sophisticated threats targeting supply chains and data fusion processes. By synthesizing current research and practical deployment insights, this review aims to provide a foundational reference for aerospace engineers, cybersecurity specialists, and policymakers, offering a roadmap to enhance the cyber-resilience of vital CNS infrastructure in an era of evolving digital threats. Full article

Multi-cloud is becoming more challenging to secure as traditional perimeter-based security models have a hard time protecting workloads running across multiple cloud platforms, identities, and services. To address this challenge, organizations are shifting to Zero-Trust Architecture (ZTA), which focuses on constant verification and stringent access control, coupled with anomaly detection methodologies to gain better visibility and threat detection in the distributed cloud environment. This paper presents a Systematic Literature Review (SLR) of anomaly detection approaches in multi-cloud environments and how these are applied in zero-trust security models. The review is conducted according to the guidelines of the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020), and is based on studies published between 2020 and 2025 selected from the databases of the following journals: Institute of Electrical and Electronics (IEEE) Xplore, Science Direct, MDPI, Google Scholar, and the Saudi Digital Library. Studies found on benchmark datasets such as CICIDS-2017 and UNSW-NB15 are not evaluated, as none addressed real multi-cloud environments. Although zero trust is highlighted in general, very few studies have implemented basics of zero trust such as micro-segmentation, identity federation, and enforcement through policy. Overall, this review identifies gaps around cross-cloud validation, explainability, and compliance-aware security design, including lack of attention to regulations such as the GDPR and HIPAA. These findings provide helpful recommendations for future research and development on practical and security solutions for multi-cloud environments. Full article

Zirconia-toughened alumina (ZTA) ceramics are promising for load-bearing biomedical applications because they combine the hardness, chemical stability, wear resistance, and biocompatibility of alumina with the transformation-toughening capability of zirconia. Grinding is indispensable for achieving dimensional accuracy and surface quality, yet it inevitably introduces surface and subsurface cracks, residual stresses, and a local tetragonal-to-monoclinic transformation of zirconia. These changes can degrade fracture toughness, increase reliability scatter, and reduce long-term service stability. Annealing is therefore often considered a post-grinding recovery strategy because it can relax residual stresses, blunt crack tips, and partially restore the zirconia phase state. However, the extent of recovery depends strongly on the initial damage state, ZTA microstructure, and thermal schedule. This review systematically summarizes the current understanding of grinding-induced damage and annealing-assisted recovery in ZTA ceramics, with particular emphasis on the coupled relationships among subsurface damage, residual-stress evolution, phase transformation, and fracture toughness. Particular attention is given to distinguishing direct ZTA-specific evidence from mechanistic interpretations inferred from related zirconia-containing ceramic systems, because datasets based exclusively on ZTA remain relatively limited. By integrating the existing evidence, this review proposes a coupled processing-damage-recovery framework and identifies the key knowledge gaps that must be addressed to achieve more reliable process optimization in advanced ZTA components. Full article

Enterprise Resource Planning (ERP) systems have long served as the primary infrastructure for internal control in financial governance, functioning as deterministic, auditable systems of record. The emergence of Zero-Trust Architecture (ZTA) and artificial intelligence (AI) progressively challenges this model, transforming enterprise systems from passive ledgers into autonomous systems of judgment capable of influencing decisions with direct financial and regulatory consequences. This study investigates how trust mediates this transformation. Drawing on a longitudinal dataset of 968 survey responses collected across five measurement waves during a ZTA deployment in a multinational telecommunications organization, we apply an extended Technology Acceptance Model (TAM) to examine changes in perceived usefulness, ease of use, and trust. The findings reveal an Audit Paradox: ZTA simultaneously strengthens formal compliance controls while eroding user trust and perceived productivity, with only partial recovery following structured governance interventions. Building on these findings, we introduce a trust-contingent framework for ERP evolution and develop the concept of the Agency Gap, a structural misalignment between algorithmic decision-making authority and institutional accountability. This study extends accounting and auditing theory into AI-driven control environments and offers practical guidance for auditors, CFOs, and technology leaders navigating the governance of increasingly autonomous digital systems. This study contributes by empirically demonstrating the trust-mediated dynamics of advanced control architectures, introducing the Agency Gap as a theoretical construct addressing algorithmic accountability in AI-driven governance, and extending Sarbanes–Oxley (SOX) oriented control theory into probabilistic, algorithmic environments. Full article

Recent research suggests a link between EBV and brain cancer, especially in high-grade gliomas, but its role has not been sufficiently elucidated. Therefore, we evaluated its occurrence in brain cancer. For this purpose, the EBV DNA and LMP-1 in tumor tissue, the level of viral load in the cerebrospinal fluid (CSF), and the serological status of patients were analyzed. We detected EBV DNA in 28.9% (42/145) of glioma samples, among which 28 were isolated from glioblastomas (GBs) and 14 from other gliomas. LMP-1 was detected in 26 (92.8%) GB samples and 5 (35.7%) samples from other gliomas. The EBV DNA load in the CSF was significantly higher in GB compared to other gliomas; anti-EBNA1, anti-EBVCA, anti-EA, and anti-Zta antibodies were detected in the serum of GB patients; and their concentration was higher in GB patients. Further research is needed to determine whether and to what extent EBV contributes to glioma development. Elucidating the role of latent EBV genes synthesized in glioblastoma is important for understanding the role of viral infection in cancer development and progression in this hitherto poorly studied area. Full article

Process parameter control is critical for reducing casting defects in ZTA2 alloy pump body investment casting. However, there exists a complex nonlinear relationship between parameters such as pouring temperature, pouring time, and shell preheating temperature, and defects including total defect volume, shrinkage porosity, and shrinkage cavities, posing significant challenges to accurate prediction and optimization. To address this issue, this study proposes an integrated strategy for defect prediction and process optimization that combines the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Backpropagation Neural Network (BP neural network). First, an L₂₅(5³) orthogonal experiment was designed, and a dataset consisting of 25 orthogonal samples and 97 random samples was constructed by combining ProCAST simulations, covering the entire parameter domain of pouring temperature, pouring time, and shell preheating temperature. Subsequently, the PSO algorithm was used to optimize the initial weights and thresholds of the BP neural network, and Bayesian regularization and 5-fold cross-validation were introduced to build a high-precision defect prediction model. The SHapley Additive exPlanations (SHAP) analysis was employed to clarify parameter sensitivity and interaction mechanisms, and the NSGA-II was combined to realize multi-objective process optimization. The results show that: compared with the traditional BP neural network, the optimized PSO-BP model improves the coefficient of determination (R²) of the test set for total defect volume prediction by 20.82% and reduces the root mean square error (RMSE) by 33.34%; for shrinkage porosity volume prediction, the R² is increased by 7.93% and the RMSE is reduced by 22.71%, which effectively solves the problems of local optimization and weak generalization ability. Pouring time is the most sensitive parameter affecting defects, and the coupling effect between pouring temperature and pouring time is the strongest. Considering actual production conditions, the superior process parameters are determined as follows: pouring temperature of 1800 °C, pouring time of 4 s, and shell preheating temperature of 475 °C. Compared with the pre-optimization results, this parameter combination reduces the total defect volume by 38.92% and the shrinkage porosity volume by 51.62%. The intelligent optimization framework constructed in this study provides reliable technical support for the accurate control of defects in ZTA2 titanium alloy pump body investment casting, and has important engineering value for improving the quality of castings in industrial production and reducing costs. Full article

The proliferation of Enterprise Networks, characterized by heterogeneous devices, distributed data sources, and increasingly sophisticated cyber threats, has exposed the limitations of traditional perimeter-based security models. Guided by the principles of Zero Trust Architecture (ZTA), this paper presents a Zero-Trust (ZT)-Driven Federated Learning Algorithm for Privacy Enhancement (ZT-FL-PE), designed to safeguard model and data confidentiality in decentralized learning environments. By integrating ZTA’s “never trust, always verify” posture with Federated Learning’s (FL) decentralized training paradigm, the proposed framework eliminates the need for centralized data aggregation and significantly reduces the attack surface. The algorithm specifically targets two prominent threats to model privacy: property inference attacks (PIAs) and membership inference attacks (MIAs). We introduce adaptive verification mechanisms and privacy-preserving update transformations that enforce continuous authentication, constrain adversarial behavior, and strengthen resilience against inference-based exploitation. Experimental results demonstrate that ZT-FL-PE substantially enhances privacy protection while maintaining high model accuracy and imposing only low-to-moderate computational overhead, making it a practical and robust solution for modern ZT Enterprise environments. Full article

The pervasive adoption of Internet of Things (IoT) devices has profoundly reshaped digital connectivity by enabling real-time data exchange and autonomous interactions on a global scale. While this transformation presents substantial operational benefits, it simultaneously introduces significant security challenges, especially in terms of Identity and Access Management (IAM) for non-human entities, such as sensors, devices, machine agents, and service accounts. Historically, traditional perimeter-based security models, which depend on static trust boundaries and implicit trust for internal actors, have been applied to human identities. However, these models prove inadequate for managing non-human identities. This inadequacy has spurred interest in Zero Trust Architecture (ZTA), an advanced security paradigm based on the principle of “never trust, always verify.” This paper examines the application of ZTA in safeguarding IoT ecosystems, with a particular emphasis on managing non-human identities. The study delves into ZTA’s fundamental principles, such as least privilege, micro-segmentation, continuous monitoring, and identity-centric access control, and evaluates their effective implementation in resource-constrained IoT settings. The research identifies critical implementation challenges and considerations for applying identity-based ZTA within IoT contexts. The findings of this paper underscore that ZTA, when meticulously implemented, provides a robust framework for mitigating the cyber risks inherent in IoT ecosystems. Furthermore, the paper delineates prospective research avenues aimed at integrating ZTA into IoT environments. Ultimately, this study contributes to the expanding body of scholarly knowledge by endorsing Zero Trust as a foundational strategy for contemporary IoT security. Full article

As a foundational protocol in wireless sensor networks (WSNs), LEACH has long contended with the dual challenges of energy load balancing and security defense. To clarify the protocol’s evolutionary trajectory within the modern IoT context, this paper presents a systematic review and restructuring of LEACH’s optimization mechanisms. The core contributions of this study are threefold: First, it establishes a taxonomy for energy optimization in LEACH. It provides an in-depth analysis of how intelligent algorithms—such as fuzzy logic and meta-heuristics—reshape cluster head election and data transmission paths in heterogeneous network environments, thereby resolving the inherent blindness of traditional mechanisms. Second, it elucidates the evolutionary patterns of LEACH security mechanisms. The paper details the transition of defense strategies from early static encryption and authentication to dynamic countermeasure mechanisms, offering a clear framework for understanding the protocol’s defensive boundaries. Finally, addressing the bottleneck where high security levels often incur high energy costs, the paper explores the feasibility of incorporating zero-trust architecture (ZTA) into WSNs within the future outlook section. This discussion aims to provide a new theoretical perspective for future research on balancing enhanced defense capabilities with energy efficiency. Full article

Inspired by the erosion-resistant dorsal armor of the desert scorpion, this study developed biomimetic ZTA ceramic composites with enhanced resistance to solid particle erosion. Three biomimetic configurations, namely convex-bump (CH-O), convex-curved-surface (CH-CS), and convex hybrid rigid–flexible (CH-HS) structures, were fabricated by direct ink writing (DIW) 3D printing. Their erosion performance was evaluated by gas–solid two-phase erosion tests at impact angles ranging from 15° to 90°, and the underlying mechanisms were elucidated through erosion morphology analysis, actual impact angle analysis, and stress-wave propagation analysis. The results showed that the erosion rate of all samples first increased and then decreased with increasing impact angle, reaching a maximum at around 60°. Compared with the smooth control sample, CH-O exhibited lower erosion resistance under low-angle erosion conditions but showed clear improvement under high-angle erosion conditions, with the erosion resistance increased by 18.39–32.54%. CH-CS further improved the erosion resistance of CH-O, with enhancements of 14.31–53.92% at low impact angles and 24.57–35.17% at high impact angles. Among all the biomimetic designs, CH-HS exhibited the best overall erosion resistance, showing an additional improvement of 9.22–32.16% over CH-CS across the tested impact angle range. The superior erosion resistance was attributed to the synergistic effects of convex-bump morphology, curved-surface-induced particle deflection, and rigid–flexible coupling. These biomimetic features modified the actual impact angle of the particles, deflected their trajectories, reduced direct particle impact, and generated a shadow effect, while the flexible layer dissipated impact energy through reflection unloading at the rigid–flexible interface. This study provides a novel strategy for the biomimetic design of erosion-resistant ceramic composites and offers new insights into mitigating erosion damage in ceramic-based mechanical components. Full article

Zero-trust architecture (ZTA) requires continuous and adaptive identity authentication to maintain security in dynamic environments. However, current federated learning (FL)-based authentication models often struggle to incorporate evolving attack patterns without experiencing catastrophic forgetting. Moreover, non-independent and identically distributed (non-IID) client data and concept drift frequently lead to degraded model robustness and personalization. To address these issues, this paper presents a hybrid learning framework that integrates federated learning with incremental learning (IL) for sustainable authentication. A Dynamic Weighted Federated Aggregation (DWFA) algorithm is developed to mitigate concept drift by adjusting aggregation weights in real time, ensuring that the global model adapts to changing data distributions. This approach enables continuous learning from distributed threat data while maintaining privacy and eliminating the need for historical data retention. Experimental results on real-world traffic datasets indicate that the proposed framework outperforms conventional FL baselines, reducing the overall error rate by approximately 56% and improving the detection rate for novel attack types by over 17.8%. Furthermore, the framework remains stable against performance decay while maintaining efficient communication overhead. This study provides an adaptive, privacy-preserving solution for identity authentication in zero-trust systems. Full article

In this study, the material removal behavior of abrasive brushing tools on zirconia-toughened alumina cutting edges is experimentally investigated. Three different brushing tool specifications with bonded diamond grains are tested, varying in filament diameter, filament length, and grain size. Using an industrial robot setup, structured brushing experiments are performed on the cutting edges of indexable inserts under controlled variations of key process parameters, such as brushing velocity v_b, axial feed rate v_fa, infeed a_e, and contact angle φ. The resulting edge rounding is quantified using three-dimensional optical scanning. Key metrics, such as edge radius r_β and form factor K, are evaluated to assess the suitability of abrasive brushing processes for the preparation of ceramic cutting edges. The results showed that the edge radius ranged from r_β = 20 to 80 µm, while the form factor varied from K = 1 to 3. The brushing velocity v_b and axial feed rate v_fa were identified as the primary parameters influencing the rounding radius r_β, whereas the infeed a_e was the dominant parameter affecting the form factor K. While cutting edge preparation of metal and carbide tools is well studied, little research exists on abrasive brushing of zirconia-toughened alumina (ZTA) cutting inserts. Because ZTA behaves differently from metals, this study systematically investigates robot-assisted abrasive brushing of ZTA, analyzing how key process parameters affect edge radius, shape, and uniformity along the cutting edge. Full article

Large organizations face a critical systems integration challenge when executing multiple concurrent security modernization programs. This paper examines the U.S. Department of Defense’s simultaneous implementation of three transformational initiatives—post-quantum cryptography (PQC) migration, Zero Trust Architecture (ZTA) deployment, and AI security assurance—each operating under separate governance structures, timelines, and compliance frameworks. Through systematic evidence synthesis of 59 sources (47 policy/standards documents and 12 performance benchmarks), we identify cross-program dependencies that create integration failures when programs operate in isolation. We propose a shared modernization substrate—a four-layer infrastructure architecture (Cryptographic Services, Identity Management, Analytics Pipeline, Policy Orchestration) that enables coordinated execution while preserving program independence. The framework addresses the fundamental systems challenge of achieving interoperability across programs with misaligned schedules and competing resource demands. We introduce a five-level Triad Convergence Maturity Model (TCMM) with operationalized indicators enabling repeatable organizational assessment. Illustrative application to three DoD modernization contexts demonstrates the framework’s ability to differentiate maturity levels. Performance analysis synthesizes published benchmark data: enterprise PQC latency overhead is modest (measured), while tactical environment estimates of 158–383% overhead are derived from benchmark extrapolation under packet-loss assumptions (modeled). Scenario modeling suggests that coordinated incident response through the substrate architecture could substantially reduce risk exposure windows compared to siloed approaches (modeled). The framework transforms fragmented program execution into synchronized systems modernization, offering practical guidance for chief information officers, program managers, and enterprise architects managing concurrent technology transitions. Full article

Multi-tenancy is essential for scalable IoT–Cloud systems; however, it introduces complex security vulnerabilities at the intersection of shared cloud infrastructures and resource-constrained IoT environments. This systematic review evaluates next-generation security frameworks designed to enforce tenant isolation without violating the strict latency (<10 ms) and energy bounds of lightweight sensors. Adhering to PRISMA guidelines, we analyze selected high-quality studies to categorize intersectional threats, including cross-tenant data leakage, side-channel attacks, and privilege escalation. Our analysis identifies a critical, unresolved conflict: existing mitigation strategies often incur a 12% computational and communication overhead, creating a significant barrier for real-time applications. Furthermore, we critically analyze emerging technologies, including Zero Trust Architectures (ZTA), adaptive Artificial Intelligence (AI), blockchain, and Post-Quantum Cryptography (PQC). We find that direct PQC deployment is currently infeasible for LPWAN protocols due to key-size constraints (1.6 KB) that exceed typical payload limits. To address these challenges, we propose a novel multi-layer security design principle that offloads heavy isolation and cryptographic workloads to hardware-accelerated edge gateways, thereby maintaining tenant isolation without compromising real-time performance. Finally, this review serves as a roadmap for future research, highlighting federated learning and hardware enclaves as essential pathways for securing next-generation multi-tenant IoT ecosystems. Full article

This study examines how the seven pillars of ZTA are implemented in a CDS environment that demands high security reliability, similar to the defense and finance sectors, and identifies the technological advancements and integration patterns that emerge during this process. With the introduction of user- and device-centric authentication methods like distributed identity and RF fingerprinting in the Identity and Device areas, there is a growing trend towards strengthening trust even in domains where distrust is prevalent. In the Network and Application domains, the focus is on using micro-segmentation and SDN to segment and control internal traffic flows, while dynamically enforcing the principle of least privilege. In the Data, Visibility, and Orchestration domains, AI analysis is being applied in real-time, leveraging log and visibility data, and orchestration is automating policy execution and response. In conclusion, it is clear that each pillar of ZTA operates in tandem with the others, rather than as isolated components within the CDS environment. This fusion structure demonstrates its ability to function as a unified security strategy that balances trust with comprehensive coverage of diverse domains. Full article